Partnering with Water Utilities for Better Decisions

Hunter Water – Strengthening Demand Forecasting for Infrastructure Planning Decisions

Hunter Water are using data mining to examine peak flow events to gain insights into demand patterns and network performance. This initiative supports their broader strategy for optimizing system operations and planning for future demand.

With advanced analytics, Hunter Water is demonstrating the value of leveraging big data to strengthen water management and infrastructure planning decisions. We commend their leadership in using data mining for demand forecasting purposes.

Wannon Water – Tackling Water Loss with Data-Driven Insights

Wannon Water established minimum night flow (MNF) baselines across selected District Metered Areas (DMAs) to quantify background leakage, monitor changes over time, and identify emerging leakage issues early to support timely intervention.

The analysis also improved Wannon Water’s visibility of hydrant extraction within DMAs, providing insights about hydrant theft in their system.

By combining leakage quantification, hydrant awareness, and consistent baseline monitoring, Wannon Water is building stronger evidence to prioritize DMAs, target maintenance activity, and reduce uncertainty in water loss decision-making.

We acknowledge Wannon Water’s leadership in applying data-driven insights to proactively manage water loss across their network.

Greater Western Water – Smarter Non-Revenue Water Management

Greater Western Water (GWW) wanted to leverage SCADA data mining to unlock deeper insights about water loss across their system.

The project involved data mining their DMAs to assess data quality and quantify non-revenue water for maintenance prioritization.

The following items were quantified for each DMA:

1. non-revenue water

2. leakage

3. pipe failures; and

4. hydrant extraction.

A report was submitted and we had an online session with 10 utility staff members, where we presented actionable insights on water loss, ranked DMAs based on non-revenue water, and provided targeted recommendations for data quality.

GWW’s leadership in harnessing data for smarter water management demonstrates how utilities can turn information into action, driving efficiency and resilience.

Greater Western Water and WaterAble – Inclusive SCADA Data

In collaboration with Greater Western Water and WaterAble, we developed an algorithm that converts SCADA time series data into descriptive HTML text.

Traditional SCADA interfaces, spreadsheets, and Power BI reports often rely on visual graphs, making it difficult for utility staff with a vision impairment to access operational data.

By embedding cleaned SCADA data into HTML, SensorClean enables a text-based narrative that improves accessibility.

This innovation also supports staff who prefer textual reports over graphical analysis, providing a versatile way to communicate SCADA insights across various business functions. We thank Greater Western Water and WaterAble for this collaboration.

Yarra Valley Water - Non-Revenue Water Analytics

Yarra Valley Water (YVW) sought to explore hydrant activity in outer-suburban DMAs while trialling our cloud-based water balance software.

To support this initiative, we developed an algorithm to identify hydrant signatures in flow data. The algorithm achieved over 90% accuracy.

Quantifying non-revenue water events from data significantly improves reporting accuracy, enhances benchmarking efforts, and supports proactive maintenance strategies.

We thank YVW for this collaboration in advancing hydrant analytics.

Intelligent Water Networks - Wastewater Pump Station Analytics

SensorClean.ai (formerly FSA Data) partnered with Intelligent Water Networks (IWN) and four Victorian utilities to deliver a Sewer Pump Station (SPS) Performance Analytics Trial, demonstrating how complex historical SCADA data can be transformed into clear, defensible evidence for planning and asset decision-making.

The project analyzed 52 SCADA data sets across 10 wastewater pump stations, including rising main flows, wet well levels, pump status, power, and rainfall. The focus was on cleaning, structuring, and interpreting historical data to reveal performance patterns typically buried in raw SCADA systems. Visualizations and key performance metrics were produced for both dry and wet weather conditions, enabling meaningful comparisons between pump stations.

Westernport Water – Using Big Data to Understand Water Age

Westernport Water was interested in advanced data analytics to understand water age in one of their reservoirs. During an Intelligent Water Networks trial, we collaborated to investigate their historical SCADA data to understand seasonal demand variations and water age.

Our collaboration led to the development of a SensorClean module that utilized Plug Flow Analysis, estimating the time required for the reservoir’s outflow to exceed its initial volume.

Westernport Water’s commitment to data-driven decision-making underscores the power of big data to optimize water storage operation to limit water age impacts.

We thank Westernport Water for this collaboration.

Goulburn Valley Water – Enhancing Data Quality for Smarter Asset Management

We worked with Goulburn Valley Water (GVW) as part of an Intelligent Water Networks (IWN) trial to improve data quality and enhance decision-making.

For the trial, 46 potable water flow data sets were cleaned and data quality issues were identified. Data was labelled in a systematic way to take advantage of machine learning projects.

The project highlighted considerable variability in sensor data quality and demonstrated how dynamic visualization of cleansed data could facilitate better collaboration between staff.

GVW sought to improve data quality and use cleaned outputs for asset decision-making and machine learning (ML) model training.

Publications

Williams G., Wang S. (2025). Using SCADA Data to Prioritise DMAs for Maintenance. IWA Water Efficiency Conference. IWA and AWA. Melbourne

Williams G., Williams B., Tondkar M., Wang S. (2024). Finding and quantifying hydrant non-revenue water. Ozwater24, AWA, Melbourne

Williams, G. and B. J. Williams (2022). Extreme Events, Big Data and Weighing Cost-Benefits on Proposed Water Infrastructure. Ozwater22, AWA, Brisbane